Even creating videos of people doing and saying things they never did (DeepFakes - a potentially nefarious application of deep learning)

Tensorflow is the world's most popular library for deep learning, and it's built by Google, whose parent Alphabet recently became the most cash-rich company in the world (just a few days before I wrote this). It is the library of choice for many companies doing AI and machine learning.

In other words, if you want to do deep learning, you gotta know Tensorflow.

This course is for beginner-level students all the way up to expert-level students. How can this be?

If you've just taken my free Numpy prerequisite, then you know everything you need to jump right in. We will start with some very basic machine learning models and advance to state of the art concepts.

Along the way, you will learn about all of the major deep learning architectures, such as Deep Neural Networks, Convolutional Neural Networks (image processing), and Recurrent Neural Networks (sequence data).

Current projects include:

Natural Language Processing (NLP)

Recommender Systems

Transfer Learning for Computer Vision

Generative Adversarial Networks (GANs)

Deep Reinforcement Learning Stock Trading Bot

Even if you've taken all of my previous courses already, you will still learn about how to convert your previous code so that it uses Tensorflow 2.0, and there are all-new and never-before-seen projects in this course such as time series forecasting and how to do stock predictions.

This course is designed for students who want to learn fast, but there are also "in-depth" sections in case you want to dig a little deeper into the theory (like what is a loss function, and what are the different types of gradient descent approaches).

Advanced Tensorflow topics include:

Deploying a model with Tensorflow Serving (Tensorflow in the cloud)

Deploying a model with Tensorflow Lite (mobile and embedded applications)

Distributed Tensorflow training with Distribution Strategies

Writing your own custom Tensorflow model

Converting Tensorflow 1.x code to Tensorflow 2.0

Constants, Variables, and Tensors

Eager execution

Gradient tape

Instructor's Note: This course focuses on breadth rather than depth, with less theory in favor of building more cool stuff. If you are looking for a more theory-dense course, this is not it. Generally, for each of these topics (recommender systems, natural language processing, reinforcement learning, computer vision, GANs, etc.) I already have courses singularly focused on those topics.

Thanks for reading, and I’ll see you in class!

Who this course is for:

Beginners to advanced students who want to learn about deep learning and AI in Tensorflow 2.0

Featured review

Tejas Suvarna
(
102 courses,
14 reviews
)

4 months ago

This is the finest course on TensorFlow you can ever get. It's a real course that covers up the complex math and the practical stuff in TensorFlow. It is a very well designed course, covers up all topics of Deep Learning with different data sets and code that we don't get elsewhere. LazyProgrammer is a true programmer and he is very authentic about the knowledge. I literally loved it. Thank you so much LazyProgrammer for this.